Volumetric measures obtained with analysis of high-definition 3D structural MRI images capture neuroanatomical variability which may be associated with long-standing traits or may help explain why certain brain areas or individuals are vulnerable to neurodegenerative processes. Volumetric measures can also demonstrate in vivo toxicity resulting from certain behaviors, such as alcohol consumption. This last year, we performed voxel based morphometry analyses on structural MRI scans to explore the neuroanatomical variability associated with long-standing personality traits, which may explain why certain individuals are vulnerable to psychiatric disease, dementia or show inefficient psychological response mechanisms. We discovered neuroanatomical differences related to the Five Factors of Personality (published in the journal Human Brain Mapping). Moreover, we performed a voxel based morphometry analysis in Baltimore Longitudinal Study of Aging (BLSA) participants and detected a significant volumetric decrease in the premotor portion of the frontal corpus callosum associated with alcohol consumption, controlling for dietary, demographic and cardiovascular risk factors (published in the journal European Neuropsychopharmacology). This finding suggests an increased vulnerability of this region, which may help explain why this same region develops demyelination and necrosis in alcoholism (Marchiafava-Bignami disease). In addition, we studied how a data reduction technique called group-level independent component analysis (ICA) may be applied to structural MRI images and generate measures for clusters of different brain areas (Independent Components). We applied ICA to MRI data collected in the ADNI study and examined how they can discriminate between participants with normal cognition, mild cognitive impairment (MCI) and Alzheimer's disease (AD). We showed that ICs can be useful as classifiers and predictors of future AD diagnosis or conversion to AD from MCI (the manuscript is currently under review). In addition, given the association between insulin resistance and Alzheimer's disease, we examined how volumetric and FDG-PET uptake measures for several key regions of interest for AD relate to peripheral insulin resistance. We found that insulin resistance promotes two abnormal and pathogenic compensations: it increases glucose metabolism in the hippocampus and medial temporal lobe at the stage of mild cognitive impairment and increases glucose metabolism in default mode network nodes at the stage of AD (the manuscript is currently under preparation for publication). Prior fMRI studies suggest that these compensatory increases in metabolism are associated with disease progression. Currently, we are performing two fMRI studies, one on cephalic insulin secretion and the other on food apetitiveness, as part of a broader study on the effects of endocannabinoid (CB1) receptor drugs on metabolism. The goal of the first study is to demonstrate a rise in insulin levels in response to food visual stimuli (cephalic insulin response) as a result of activation of certain brain areas (insula, anterior cingulate, hypothalamus, ventral tegmental area, etc). Moreover, given the presence of CB1 receptors in the candidate areas, we aim to demonstrate a difference in their level of activation with CB1 agonists and antagonists. The goal of the second study is to demonstrate dissociable effects of CB1 receptor stimulation on food value (food choices) and salience (intensity of such choices).